A Comparative Study of Twitfeel and Transformer-Based Techniques for the Analysis of Text Data for Sentiment Classification
Aarshitha Vemulapalli, Anudeep Peddi · 2023
Sentiment analysis is an important task in the field of Natural Language Processing (NLP) with various applications such as understanding customer feedback and monitoring social media sentiment. In recent years, the development of transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) has revolutionized the field of NLP, achieving state-of-the-art results in a wide range of tasks, including sentiment analysis. This work presents a comparative study between traditional techniques versus transformer-based models like BERT highlighting the advantages and limitations of each.